The benefits of interim adaptations come with certain costs as the distributions of sample-based statistics changes [[1]}, [2]}]. For example, unadjusted for multiple testing Bayesian procedures do not control type 1 error [see Table 18.1 in [3]}], maximum likelihood estimators become biased [[4]}], and Wald confidence intervals do not provide the desired coverage after GSDs or SSRs.
In this paper we use Fisher Information to quantify the information loss associated with interim decisions using interim data that depends on the parameter of interest.
